The Center for Computational Psychiatry investigates

how quantitative, objective tools and methodologies can be used

to enhance mental health diagnosis and treatment.

Laboratories

 Berner Laboratory 

Investigating the learning, decision-making, and sensing processes that govern self-control, and how these processes differ in individuals with eating pathology

Integrating multi-modal data from the lab and in the wild—including functional and structural brain imaging, ecological momentary assessment, and neuroendocrine measures—with computational models to pinpoint drivers of dysregulated eating

Developing and testing new brain modulation approaches for treating disorders and symptoms characterized by “losses of control”

 Schiller Laboratory 

Uncovering how the brain encodes, stores, and retrieves emotional experiences, particularly fear and trauma, and how these memories can be modified

Investigating how the brain processes social information and tracks social relationships

Developing new therapeutic approaches for treating emotional and social disorders, while enhancing our overall understanding of the human affectome

 Fiore Laboratory 

Modeling choice behavior and associated neural dynamics using computational models based on Bayesian inference, reinforcement learning and bio-inspired artificial neural networks

Analysing cortico-striatal brain circuits, and how their disruption underlies disorders characterised by compulsive behaviours such as substance use disorders, behavioural addictions. eating disorders or OCD

Formally describing disorder specific and transdiagnostic neurocomputational mechanisms in terms of state transition and circuit stability or instability

 Rhoads Laboratory 

Investigating the neural, cognitive, and computational basis of social connection and interaction

Leveraging theory-driven computational models to characterize how multi-agent interactions shape well-being across different contexts and timescales

Examining variability in how the human brain learns and represents of others’ internal states—such as their emotions, beliefs, or goals—to guide social decisions

 Radulescu Laboratory 

Building computational models that predict real-time behavior from data such as choices, eye movements, and virtual reality interactions.

Applying reinforcement learning and Bayesian inference to reveal the algorithms driving natural behavior

Collaborating with neuroscientists to investigate how these processes are implemented in the brain

 Saez Laboratory 

Deploying cutting-edge neurotechnology (e.g. intracranial EEG, targeted neuromodulation) in humans to both monitor and intervene in brain circuits in real time

Understanding how the brain chooses actions: which brain circuits and neurotransmitter systems drive decisions, and how these relate to normal vs pathological cognitive states

Studying how disruptions in choice behavior manifest in disorders (e.g. depression, anxiety) and aim to develop neuromodulatory interventions to “repair” or ameliorate dysfunctional cognition.

Publications

For the complete CCP collection, visit our portal below  

Center for Computational Psychiatry on the Icahn School of Medicine Scholars Portal

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